Manuscript Due

Oct. 30, 2018 (closed)


In recent years, the deluge of visual data explosively aggregates at online information sites in a variety of diverse forms, such as image, video, multiple view, multiple modality, etc. which brings great challenges to the retrieval, analysis and understanding. In practice, many tasks upon such data (e.g., content-based image retrieval, object recognition, etc.) heavily rely on the modeling and representation of the big visual data. In the literature, there exist a large number of studies that have attempted to pursue the efficient model or representation, by introducing techniques like compact feature learning, content analysis, scene understanding, and so on. However, questions and challenges brought by the emerging large-scale visual applications still remain to be answered in this area, and require the intensive and multidisciplinary research. The aim of this special issue is to provide a forum for related researchers to share the most state-of-the-art development in modeling and representation for big visual data, with emphasis on their applications to addressing diverse large-scale visual problems.

Topics of interest include:
  • Feature learning for large scale visual understanding
  • Hierarchical representation learning via deep learning
  • Deep content modelling for images and videos
  • Deep hashing for fast image/video retrieval
  • Large-scale visual recognition in the wild
  • Cross-modal modelling and representation of visual data
  • Semantic feature learning for affective computing
  • Social media mining and understanding
  • Visualization of deep learning networks
  • Visualization of virtual/augmented reality big data
  • Quality-aware feature learning for visual understanding
  • Compact descriptors for mobile applications

Lead Guest Editor

  • Leida Li, China University of Mining and Technology

Guest Editors

  • Cheng Deng, Xidian University
  • Xianglong Liu, Beihang University
  • Yan Wang, Johns Hopkins University
  • Hantao Liu, Cardiff University
EURASIP Journal on Image and Video Processing